Genome sequence of Acinetobacter baumannii TYTH-1.

Acinetobacter baumannii has emerged recently as a major cause of health care-associated infections due to the extent of its antimicrobial resistance and its propensity to cause large nosocomial outbreaks. Here we report the genome sequence of Acinetobacter baumannii TYTH-1 isolated in Taiwan during 2008.

Mixed sequence reader: a program for analyzing DNA sequences with heterozygous base calling.

The direct sequencing of PCR products generates heterozygous base-calling fluorescence chromatograms that are useful for identifying single-nucleotide polymorphisms (SNPs), insertion-deletions (indels), short tandem repeats (STRs), and paralogous genes. Indels and STRs can be easily detected using the currently available Indelligent or ShiftDetector programs, which do not search reference sequences. However, the detection of other genomic variants remains a challenge due to the lack of appropriate tools for heterozygous base-calling fluorescence chromatogram data analysis. In this study, we developed a free web-based program, Mixed Sequence Reader (MSR), which can directly analyze heterozygous base-calling fluorescence chromatogram data in .abi file format using comparisons with reference sequences. The heterozygous sequences are identified as two distinct sequences and aligned with reference sequences. Our results showed that MSR may be used to (i) physically locate indel and STR sequences and determine STR copy number by searching NCBI reference sequences; (ii) predict combinations of microsatellite patterns using the Federal Bureau of Investigation Combined DNA Index System (CODIS); (iii) determine human papilloma virus (HPV) genotypes by searching current viral databases in cases of double infections; (iv) estimate the copy number of paralogous genes, such as ß-defensin 4 (DEFB4) and its paralog HSPDP3.

Detection of exercise periodic breathing using thermal flowmeter in patients with heart failure.

Exercise periodic breathing (EPB) is associated with exercise intolerance and poor prognosis in patients with heart failure (HF). However, EPB detection during cardiopulmonary exercise test (CPET) is difficult. The present study investigated the use of a wireless monitoring device to record the EPB during CPET and proposed quantization parameter estimates for the EPB. A total of 445 patients with HF were enrolled and underwent exercise tests. The ventilation data from the wearable device were compared with the data obtained during the CPET and were analyzed based on professional opinion and on 2 automated programs (decision tree [DT] and oscillatory pattern methods). The measurement accuracy was greater with the DT method (89 %) than with the oscillatory pattern method (75 %). The cutoffs for EPB recognition using the DT method were (1) an intercept of the regression line passing through the minute ventilation rate vs. the time curve during the recovery phase ≥64.63, and (2) an oscillatory phase duration to total exercise time ratio ≥0.5828. The wearable device was suitable for the assessment of EPB in patients with HF, and our new automated analysis system using the DT method effectively identified the EPB pattern.

A reinforced merging methodology for mapping unique peptide motifs in members of protein families.

BACKGROUND: Members of a protein family often have highly conserved sequences; most of these sequences carry identical biological functions and possess similar three-dimensional (3-D) structures. However, enzymes with high sequence identity may acquire differential functions other than the common catalytic ability. It is probable that each of their variable regions consists of a unique peptide motif (UPM), which selectively interacts with other cellular proteins, rendering additional biological activities. The ability to identify and localize such UPMs is paramount in recognizing the characteristic role of each member of a protein family. RESULTS: We have developed a reinforced merging algorithm (RMA) with which non-gapped UPMs were identified in a variety of query protein sequences including members of human ribonuclease A (RNaseA), epidermal growth factor receptor (EGFR), matrix metalloproteinase (MMP), and Sma-and-Mad related protein families (Smad). The UPMs generally occupy specific positions in the resolved 3-D structures, especially the loop regions on the structural surfaces. These motifs coincide with the recognition sites for antibodies, as the epitopes of four monoclonal antibodies and two polyclonal antibodies were shown to overlap with the UPMs. Most of the UPMs were found to correlate well with the potential antigenic regions predicted by PROTEAN. Furthermore, an accuracy of 70% can be achieved in terms of mapping a UPM to an epitope. CONCLUSION: Our study provides a bioinformatic approach for searching and predicting potential epitopes and interacting motifs that distinguish different members of a protein family.

Simpute: an efficient solution for dense genotypic data.

Single nucleotide polymorphism (SNP) data derived from array-based technology or massive parallel sequencing are often flawed with missing data. Missing SNPs can bias the results of association analyses. To maximize information usage, imputation is often adopted to compensate for the missing data by filling in the most probable values. To better understand the available tools for this purpose, we compare the imputation performances among BEAGLE, IMPUTE, BIMBAM, SNPMStat, MACH, and PLINK with data generated by randomly masking the genotype data from the International HapMap Phase III project. In addition, we propose a new algorithm called simple imputation (Simpute) that benefits from the high resolution of the SNPs in the array platform. Simpute does not require any reference data. The best feature of Simpute is its computational efficiency with complexity of order (mw + n), where n is the number of missing SNPs, w is the number of the positions of the missing SNPs, and m is the number of people considered. Simpute is suitable for regular screening of the large-scale SNP genotyping particularly when the sample size is large, and efficiency is a major concern in the analysis.